Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach

• URL: http://arxiv.org/abs/2512.09198v1
• Date: Tue, 09 Dec 2025 23:46:46 GMT
• Title: Towards Optimal Valve Prescription for Transcatheter Aortic Valve Replacement (TAVR) Surgery: A Machine Learning Approach
• Authors: Phevos Paschalidis, Vasiliki Stoumpou, Lisa Everest, Yu Ma, Talhat Azemi, Jawad Haider, Steven Zweibel, Eleftherios M. Protopapas, Jeff Mather, Maciej Tysarowski, George E. Sarris, Robert C. Hagberg, Howard L. Haronian, Dimitris Bertsimas,
• Abstract summary: Transcatheter Aortic Valve Replacement (TAVR) has emerged as a minimally invasive treatment option for patients with severe aortic stenosis.<n>Current guidelines regarding valve type prescription remain an active topic of debate.<n>We propose a data-driven tool to identify the optimal valve type to minimize the risk of permanent pacemaker implantation.
• Score: 3.4414136502641406
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Transcatheter Aortic Valve Replacement (TAVR) has emerged as a minimally invasive treatment option for patients with severe aortic stenosis, a life-threatening cardiovascular condition. Multiple transcatheter heart valves (THV) have been approved for use in TAVR, but current guidelines regarding valve type prescription remain an active topic of debate. We propose a data-driven clinical support tool to identify the optimal valve type with the objective of minimizing the risk of permanent pacemaker implantation (PPI), a predominant postoperative complication. We synthesize a novel dataset that combines U.S. and Greek patient populations and integrates three distinct data sources (patient demographics, computed tomography scans, echocardiograms) while harmonizing differences in each country's record system. We introduce a leaf-level analysis to leverage population heterogeneity and avoid benchmarking against uncertain counterfactual risk estimates. The final prescriptive model shows a reduction in PPI rates of 26% and 16% compared with the current standard of care in our internal U.S. population and external Greek validation cohort, respectively. To the best of our knowledge, this work represents the first unified, personalized prescription strategy for THV selection in TAVR.

Related papers

• Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation [5.4128175267869265]
  Severe aortic stenosis is a common and life-threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI)<n>Despite procedural advances, paravalvular aortic regurgitation (PVR) remains one of the most frequent post-TAVI complications.<n>In this work, we investigate the potential of deep learning to predict the occurrence of PVR from preoperative cardiac CT.
  arXiv  Detail & Related papers  (2026-02-14T18:09:27Z)
• An Interpretable AI Tool for SAVR vs TAVR in Low to Intermediate Risk Patients with Severe Aortic Stenosis [3.1857591443934816]
  We introduce an interpretable prescriptive framework that integrates prognostic matching, counterfactual outcome modeling, and an Optimal Policy Tree (OPT)<n>If the OPT prescriptions are applied, counterfactual evaluation showed an estimated reduction in 5-year mortality of 20.3% in Hartford and 13.8% in St. Vincent's relative to real-life prescriptions.
  arXiv  Detail & Related papers  (2025-12-11T05:54:22Z)
• Semantic Segmentation for Preoperative Planning in Transcatheter Aortic Valve Replacement [61.573750959726475]
  We consider medical guidelines for preoperative planning of the transcatheter aortic valve replacement (TAVR) and identify tasks that may be supported via semantic segmentation models.<n>We first derive fine-grained TAVR-relevant pseudo-labels from coarse-grained anatomical information, in order to train segmentation models and quantify how well they are able to find these structures in the scans.
  arXiv  Detail & Related papers  (2025-07-22T13:24:45Z)
• Adaptable Cardiovascular Disease Risk Prediction from Heterogeneous Data using Large Language Models [70.64969663547703]
  AdaCVD is an adaptable CVD risk prediction framework built on large language models extensively fine-tuned on over half a million participants from the UK Biobank.<n>It addresses key clinical challenges across three dimensions: it flexibly incorporates comprehensive yet variable patient information; it seamlessly integrates both structured data and unstructured text; and it rapidly adapts to new patient populations using minimal additional data.
  arXiv  Detail & Related papers  (2025-05-30T14:42:02Z)
• DeepVox and SAVE-CT: a contrast- and dose-independent 3D deep learning approach for thoracic aorta segmentation and aneurysm prediction using computed tomography scans [2.3135717943756307]
  Thoracic aortic aneurysm (TAA) is a fatal disease which potentially leads to dissection or rupture through progressive enlargement of the aorta. Scans for other indications could help on this screening, however if acquired without contrast enhancement or with low dose protocol, it can make the clinical evaluation difficult. In this study, it was selected 587 unique CT scans including control and TAA patients, acquired with low and standard dose protocols, with or without contrast enhancement. A novel segmentation model, DeepVox, exhibited dice score coefficients of 0.932 and 0.897 for development and test sets, respectively, with faster training speed in comparison to models
  arXiv  Detail & Related papers  (2023-10-23T19:48:58Z)
• Deep Metric Learning for the Hemodynamics Inference with Electrocardiogram Signals [14.972877620537686]
  We use a dataset that contains over 5.4 million ECGs without concomitant central pressure labels to pre-train a self-supervised DML model. A supervised DML model that uses ECGs with access to 8,172 mPCWP labels demonstrated significantly better performance on the mPCWP regression task.
  arXiv  Detail & Related papers  (2023-08-09T01:30:07Z)
• A Conditional Flow Variational Autoencoder for Controllable Synthesis of Virtual Populations of Anatomy [76.20367415712867]
  We propose a conditional variational autoencoder (cVAE) with normalising flows to boost the flexibility and complexity of the approximate posterior learnt. We demonstrate the performance of our conditional flow VAE using a data set of cardiac left ventricles acquired from 2360 patients.
  arXiv  Detail & Related papers  (2023-06-26T13:23:52Z)
• Building Brains: Subvolume Recombination for Data Augmentation in Large Vessel Occlusion Detection [56.67577446132946]
  A large training data set is required for a standard deep learning-based model to learn this strategy from data. We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients. In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
  arXiv  Detail & Related papers  (2022-05-05T10:31:57Z)
• Personalized Deep Learning for Ventricular Arrhythmias Detection on Medical IoT Systems [17.966382901357118]
  Life-threatening ventricular arrhythmias (VA) are the leading cause of sudden cardiac death (SCD) We propose the personalized computing framework for deep learning based VA detection on medical IoT systems. We equip the system with real-time inference on both intracardiac and surface rhythm monitors.
  arXiv  Detail & Related papers  (2020-08-18T17:41:58Z)
• Neural collaborative filtering for unsupervised mitral valve segmentation in echocardiography [60.08918310097638]
  We propose an automated and unsupervised method for the mitral valve segmentation based on a low dimensional embedding of the echocardiography videos. The method is evaluated in a collection of echocardiography videos of patients with a variety of mitral valve diseases and on an independent test cohort. It outperforms state-of-the-art emphunsupervised and emphsupervised methods on low-quality videos or in the case of sparse annotation.
  arXiv  Detail & Related papers  (2020-08-13T12:53:26Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• Gradient Boosting on Decision Trees for Mortality Prediction in Transcatheter Aortic Valve Implantation [5.050648346154715]
  Current prognostic risk scores in cardiac surgery are based on statistics and do not yet benefit from machine learning. This research aims to create a machine learning model to predict one-year mortality of a patient after TAVI.
  arXiv  Detail & Related papers  (2020-01-08T10:04:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.